In a previous article, I laid out the four ways I see people use the term AI. To summarize, the first three are quite specific. When people say “AI,” they sometimes mean Artificial General Intelligence (the research area working to replicate general human intelligence), Deep Learning algorithms to solve complex problems, or Generative AI (like ChatGPT or the related text-to-image drawing).
Well done article. Observe Irv uses a broad definition of optimization which is important. My experience over 50 years is operations research starting point are the decisions the business or organization needs to make the structure of the network (Karl Kemp term) that bounds the decision. It then applies a search process to investigate alternatives. The "model" becomes the focal point to get various groups to collaborate. If you don't understand the kempf-sullivan decision grid, the discussion becomes a free for all.
Mike, I find this topic fascinating because it is so important to be able to find a good consistent framework and set of definitions to communicate this back up to the Leaders that be, that many times, aren't close enough to it to understand it. I have seen the traditional three, descriptive, predictive, prescriptive analytics get adjusted to include diagnostic analytics. I have recently seen the addition of cognitive analytics that looks to cover image recognition. I see that you are coming at this from the often abused and wide net of a term AI. I wonder if a separation is research vs solving business problems. I believe the former would include Artificial General Intelligence while the latter would include Generative AI and Practical/Enterprise AI. It is under that last one that would be subdivided (optimization - you know you love that I put that 1st!, forecasting, prediction, NLP, image recognition, conversational ai, generative ai). All good stuff! Hope to run into you soon!
Well done article. Observe Irv uses a broad definition of optimization which is important. My experience over 50 years is operations research starting point are the decisions the business or organization needs to make the structure of the network (Karl Kemp term) that bounds the decision. It then applies a search process to investigate alternatives. The "model" becomes the focal point to get various groups to collaborate. If you don't understand the kempf-sullivan decision grid, the discussion becomes a free for all.
Mike, I find this topic fascinating because it is so important to be able to find a good consistent framework and set of definitions to communicate this back up to the Leaders that be, that many times, aren't close enough to it to understand it. I have seen the traditional three, descriptive, predictive, prescriptive analytics get adjusted to include diagnostic analytics. I have recently seen the addition of cognitive analytics that looks to cover image recognition. I see that you are coming at this from the often abused and wide net of a term AI. I wonder if a separation is research vs solving business problems. I believe the former would include Artificial General Intelligence while the latter would include Generative AI and Practical/Enterprise AI. It is under that last one that would be subdivided (optimization - you know you love that I put that 1st!, forecasting, prediction, NLP, image recognition, conversational ai, generative ai). All good stuff! Hope to run into you soon!